mlsanityData to 14 Oct 2026

Time Series (re)sampling using Generative Adversarial Networks

arXiv 2102.00208 ↗First posted 30 Jan 2021cs.LG econ.EM stat.MEcited by 4

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Abstract (arXiv)

We propose a novel bootstrap procedure for dependent data based on Generative Adversarial networks (GANs). We show that the dynamics of common stationary time series processes can be learned by GANs and demonstrate that GANs trained on a single sample path can be used to generate additional samples from the process. We find that temporal convolutional neural networks provide a suitable design for the generator and discriminator, and that convincing samples can be generated on the basis of a vector of iid normal noise. We demonstrate the finite sample properties of GAN sampling and the suggested bootstrap using simulations where we compare the performance to circular block bootstrapping in the case of resampling an AR(1) time series processes. We find that resampling using the GAN can outperform circular block bootstrapping in terms of empirical coverage.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 40 references that are in the map. First posted on arXiv 30 Jan 2021.

Builds on

20 of 40 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Improved Techniques for Training GANscited by 2,970
  3. 2017Improved Training of Wasserstein GANscited by 2,235
  4. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  5. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  6. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
  7. 2017Wasserstein GANcited by 639
  8. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 333
  9. 2017GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibriumcited by 213
  10. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
  11. 2019Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecastingcited by 161
  12. 2017Conditional Time Series Forecasting with Convolutional Neural Networkscited by 123
8 more
  1. 2019Quant GANs: Deep Generation of Financial Time Seriescited by 108
  2. 2020Conditional GAN for timeseries generationcited by 37
  3. 2019Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulationscited by 32
  4. 2020Some Theoretical Insights into Wasserstein GANscited by 28
  5. 2018Some Theoretical Properties of GANscited by 27
  6. 2019Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combinationcited by 22
  7. 2020Decision-Aware Conditional GANs for Time Series Datacited by 11
  8. 2020Statistical analysis of Wasserstein GANs with applications to time series forecastingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 2.

4 papers.

  1. 2026Improving Machine Learning Performance with Synthetic Augmentationcited by 1
  2. 2023TCGAN: Convolutional Generative Adversarial Network for Time Series Classification and Clusteringcited by 0
  3. 2023A Spatiotemporal Deep Neural Network for Fine-Grained Multi-Horizon Wind Predictioncited by 0
  4. 2026Generative Predictive Distributions for Time Seriescited by 0